no code implementations • 18 Sep 2023 • Benyang Gong, Jiacheng He, Gang Wang, Bei Peng
This brief optimizes TKF by using the Gaussian mixture model(GMM), which generates a reasonable covariance matrix from the measurement noise to replace the one used in the existing algorithm and breaks the adjustment limit of the confidence level.
no code implementations • 15 Sep 2023 • Jiacheng He, Shan Zhong, Bei Peng, Gang Wang, Qizhen Wang
In multi-target tracking (MTT), non-Gaussian measurement noise from sensors can diminish the performance of the Gaussian-assumed Gaussian mixture probability hypothesis density (GM-PHD) filter.
no code implementations • 6 Sep 2023 • Zuxuan Zhang, Gang Wang, Jiacheng He, Shan Zhong
The estimation of non-Gaussian measurement noise models is a significant challenge across various fields.
no code implementations • 4 Jul 2023 • Jiacheng He, Bei Peng, Zhenyu Feng, Xuemei Mao, Song Gao, Gang Wang
In this paper, a generalized packet drop model is proposed to describe the packet loss phenomenon caused by DoS attacks and other factors.
no code implementations • 20 Jun 2023 • Xuemei Mao, Gang Wang, Bei Peng, Jiacheng He, Kun Zhang, Song Gao
A DKF, called model fusion DKF (MFDKF) is proposed against the non-Gaussain noise.
no code implementations • 14 Jan 2023 • Jiacheng He, Gang Wang, Xuemei Mao, Song Gao, Bei Peng
Distributed Kalman filter approaches based on the maximum correntropy criterion have recently demonstrated superior state estimation performance to that of conventional distributed Kalman filters for wireless sensor networks in the presence of non-Gaussian impulsive noise.
no code implementations • 14 Jan 2023 • Jiacheng He, Hongwei Wang, Gang Wang, Shan Zhong, Bei Peng
Outliers and impulsive disturbances often cause heavy-tailed distributions in practical applications, and these will degrade the performance of Gaussian approximation smoothing algorithms.
1 code implementation • 8 Sep 2021 • Jiacheng He, Gang Wang, Bei Peng, Zhenyu Feng, Kun Zhang
In our study, a novel concept, called generalized error entropy, utilizing the generalized Gaussian density (GGD) function as the kernel function is proposed.
